Learning when to say no

We consider boundedly-rational agents in McCall's model of intertemporal job search. Agents update over time their perception of the value of waiting for an additional job offer using value-function learning. A first-principles argument applied to a stationary environment demonstrates asymptotic convergence to fully optimal decision-making. In environments with actual or possible structural change our agents are assumed to discount past data. Using simulations, we consider a change in unemployment benefits, and study the effect of the associated learning dynamics on unemployment and its duration. Separately, in a calibrated exercise we show the potential of our model of bounded rationality to resolve a frictional wage dispersion puzzle. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

The study focuses on boundedly-rational agents in McCall's model, who update their perception of waiting for job offers using value-function learning. The model demonstrates asymptotic convergence to fully optimal decision-making in stationary and changing environments, and explores the impact of learning dynamics on unemployment duration and wage dispersion puzzle resolution in simulations and calibration exercises.